人工智能和风险的可代表观
Andy Crabtree1, Glenn McGarry1, Lachlan Urquhart2
1School of Computer Science, University of Nottingham, Nottingham, UK.
概括
人工智能 (AI) 风险管理依赖于实际的,超出一般框架的具体行动. 了解这些"可重复的表观"可以改善人工智能安全和开发实践.
科学领域:
- 计算机科学 计算机科学
- 技术社会学技术的社会学.
- 风险管理 风险管理
背景情况:
- 目前的AI风险管理依赖于诸如影响评估和法规等一般框架.
- 这些"通用计算"方法可能无法完全捕捉到风险实际上是如何理解和管理的.
- 在理解人工智能开发人员,监管机构和网络安全专家使用的实际,定位方法方面存在差距.
研究的目的:
- 探索AI开发人员,监管机构和网络安全专家如何在实践中捕捉和管理风险.
- 确定现有的"通用微积分"框架在现实世界AI风险管理中的局限性.
- 揭示"处境实践"和"可循环表征"在AI风险感知和应对中的作用.
主要方法:
- 对风险认识和管理实践的定性分析.
- 审查监管机构,开发人员和网络安全专家的观点.
- 在定位的AI开发环境中识别和阐述"可演变的表观".
主要成果:
- 人工智能中的风险管理受到一般框架不涵盖的平凡,定位实践的严重影响.
- 人工智能专业人员开发和利用"可重复的表观"来理解和应对风险.
- 这些定位实践揭示了人工智能风险管理人员面临的重大挑战.
结论:
- 对人工智能风险的"一般计算"是不够的;定位实践和"可循环表征"至关重要.
- 了解这些实际元素为AI风险的跨学科研究提供了途径.
- 详细阐述"可重复的表观"可以提高人工智能开发人员的风险意识,并为不断发展的风险管理框架提供信息.
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